Adherence to Antiretrovirals Among US Women During and After Pregnancy
Bibliographic record
Abstract
BACKGROUND: Antiretrovirals (ARVs) are recommended for maternal health and to reduce HIV-1 mother-to-child transmission, but suboptimal adherence can counteract its benefits. OBJECTIVES: To describe antepartum and postpartum adherence to ARV regimens and factors associated with adherence. METHODS: We assessed adherence rates among subjects enrolled in Pediatric AIDS Clinical Trials Group Protocol 1,025 from August 2002 to July 2005 on tablet formulations with at least one self-report adherence assessment. Perfectly adherent subjects reported no missed doses 4 days before their study visit. Generalized estimating equations were used to compare antepartum with postpartum adherence rates and to identify factors associated with perfect adherence. RESULTS: Of 519 eligible subjects, 334/445 (75%) reported perfect adherence during pregnancy. This rate significantly decreased 6, 24, and 48 weeks postpartum [185/284 (65%), 76/118 (64%), and 42/64 (66%), respectively (P < 0.01)]. Pregnant subjects with perfect adherence had lower viral loads. The odds of perfect adherence were significantly higher for women who initiated ARVs during pregnancy (P < 0.01), did not have AIDS (P = 0.02), never missed prenatal vitamins (P < 0.01), never used marijuana (P = 0.05), or felt happy all or most of the time (P < 0.01). CONCLUSIONS: Perfect adherence to ARVs was better antepartum, but overall rates were low. Interventions to improve adherence during pregnancy are needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".